CN111598425A - Order flow control method and device - Google Patents

Order flow control method and device Download PDF

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CN111598425A
CN111598425A CN202010382621.6A CN202010382621A CN111598425A CN 111598425 A CN111598425 A CN 111598425A CN 202010382621 A CN202010382621 A CN 202010382621A CN 111598425 A CN111598425 A CN 111598425A
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张逾
付岩
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Rajax Network Technology Co Ltd
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Abstract

The application discloses a order flow control method, which comprises the following steps: obtaining historical performance data, current performance data and current performance trend data of the target entity object; determining an order threshold of the target entity object in a specific time interval according to the historical fulfillment data, the current fulfillment data and the current fulfillment trend data, wherein the order threshold is a threshold for setting an upper limit of an order quantity generated in the specific time interval; and limiting the number of orders generated by the target entity object in the specific time interval according to the order threshold. By adopting the method, the problem of low flow control accuracy of the order generation of the target entity object is solved.

Description

Order flow control method and device
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, and a device for order flow control. The application also relates to a order flow control system.
Background
With the popularization of the internet, more and more users carry out online transactions. The user places an order for the business object provided by the entity object on the network, and the completion of the distribution of the business object corresponding to the order is the completion of one performance. The number of orders, the delivery capacity and the providing capacity of the business objects are important factors influencing the non-fulfillment rate of the entity objects, and the non-fulfillment rate is higher, so that the invalid order rate is increased. Therefore, order thresholds are generally used to flow control the generation of orders for physical objects to reduce the invalid order rate. When the matching degree of the order threshold value used for order flow control and the actual performance capability of the entity object is higher, the order distribution of the entity object can be more reasonable, and meanwhile, the invalid order rate can be kept lower, so that the user experience is improved.
In the prior art, a day is generally divided into a plurality of time slices, and the order threshold value of each time slice is set as the performance average value of the historical time slice corresponding to each time slice. For example, 24 time slices are divided in 24 hours a day, the performance condition of each time slice is counted, and the order threshold value of the time slice on the day is estimated by combining the change trend of the order quantity order on the day; and adjusting the order threshold value of the future time slice of the current day every half hour according to the current day performance condition of the entity object, doubling the order threshold value if the current day real-time performance rate is high, and reducing the order threshold value to half if the current day real-time performance rate is low. There are the following problems: the average order quantity of historical statistics is used as an order threshold value, the historical statistics threshold value is too dependent on, and the actual performance situation is difficult to respond in time, so that the order threshold values of most entity objects are limited to the historical performance threshold value. The adjustment method of the order threshold value is not flexible enough, and the order flow control is not accurate enough, so that the matching degree of the order threshold value and the actual performance capability of the entity object is low.
Therefore, how to perform high-accuracy flow control on order generation of target entity objects is a problem to be solved.
Disclosure of Invention
The order flow control method provided by the embodiment of the application provides a more reasonable order flow control scheme, and solves the problem of low flow control accuracy of order generation of the target entity object.
An embodiment of the present application provides a order flow control method, including: obtaining historical performance data, current performance data and current performance trend data of the target entity object; determining an order threshold of the target entity object in a specific time interval according to the historical fulfillment data, the current fulfillment data and the current fulfillment trend data, wherein the order threshold is a threshold for setting an upper limit of an order quantity generated in the specific time interval; and limiting the number of orders generated by the target entity object in the specific time interval according to the order threshold.
Optionally, the obtaining current performance data of the target entity object includes: extracting order information of the target entity object in real time according to the service log, and transmitting the obtained real-time order data stream into a dynamic database; the dynamic database is used for storing order information generated in the current time period from the preset time to the present time; and extracting the order quantity and the order cancellation information of the target entity object from the order information stored in the dynamic database, and obtaining the order non-fulfillment rate in the current time period as the current fulfillment data according to the order quantity and the order cancellation information.
Optionally, the obtaining of the historical fulfillment data of the target entity object includes: acquiring historical order information of the target entity object according to a preset period and a service log, and storing the acquired historical order information into an offline database; and extracting the historical order quantity and the historical order canceling information of the target entity object from the order information stored in the off-line database, and obtaining the historical order non-fulfillment rate of a specified historical time period according to the historical order quantity and the historical order canceling information to serve as the historical fulfillment data.
Optionally, the obtaining current performance trend data of the target entity object includes: dividing the current time period into a plurality of time intervals according to a preset time interval; aiming at the target entity object, acquiring the difference between the order cancellation rates of the current time interval and the previous adjacent time interval as current performance trend data; or, for the target entity object, acquiring a difference between the order cancellation rates of the current time interval and a specified time interval before the current time interval as current performance trend data.
Optionally, the method further includes: determining a pre-estimated value of the duration of performance of the target entity object according to the historical performance data; and determining a specific time interval corresponding to the current time according to the achievement duration estimated value.
Optionally, the determining the predicted value of the duration of performance of the target entity object according to the historical performance data includes: obtaining historical picking duration and historical distribution duration of the target entity object according to the historical fulfillment data of the target entity object; and estimating a current performing duration pre-estimated value according to the historical picking duration and the historical distribution duration.
Optionally, the method further includes: determining an initial value of an order threshold value of the target entity object in the specific time interval according to historical performance data corresponding to the specific time interval; the determining the order threshold of the target entity object in the specific time interval includes: adjusting the initial value of the order threshold value according to a preset adjusting and controlling period by adopting a specific algorithm to obtain the order threshold value of the target entity object in a specific time interval; the specific algorithm is an algorithm for adjusting the order threshold according to the historical performance data, the current performance data and the current performance trend data.
Optionally, the method further includes: determining the service type of the target entity object; and adjusting the initial value of the order threshold value according to the service type of the target entity object to obtain the real-time order threshold value of the specific time interval.
Optionally, the adjusting the initial value of the order threshold according to the service type to which the target entity object belongs to obtain the real-time order threshold of the specific time interval includes: obtaining the proportion of the ideal cancellation rate and the historical smooth cancellation rate of the service type as a first factor; obtaining the proportion of the ideal cancellation rate and the current cancellation rate of the service type as a second factor; obtaining an expansion value of the current performance trend data as a third factor; determining an index for calculating a real-time order threshold according to the first factor and the weight corresponding to the first factor, the second factor and the weight corresponding to the second factor, and the third factor and the weight corresponding to the third factor; and taking the initial value of the order threshold value as a base number, and calculating by using the index to obtain the real-time order threshold value of the specific time interval.
Optionally, the method further includes: if the difference between the newly generated order threshold value and the order threshold value of the last regulation and control period is determined, generating an order threshold value updating message according to the newly generated order threshold value, and providing the updating message to an order generating unit for generating an order aiming at the target entity object through a message queue; the newly generated order threshold is parsed out and used for limiting the number of orders generated in a specific time interval.
Optionally, the method further includes: and if the target entity object is determined to be the entity object with the specific level, receiving an input trigger of order threshold control aiming at the target entity object, and canceling the order flow control according to the input trigger.
The embodiment of the present application further provides an order flow control system, which includes: the system comprises a dynamic database, an off-line database, a data processing unit, an intelligent threshold value calculating unit and a transaction unit; the dynamic database is used for storing a real-time order data stream of a target entity object, and providing online current performance information by matching with the data processing unit; the off-line database is used for storing an off-line order data stream of the target entity object and providing off-line historical performance information by matching the data processing unit; the data processing unit is used for acquiring online current fulfillment information provided by the dynamic database to obtain current fulfillment data and current fulfillment trend data of the target entity object; acquiring offline historical fulfillment information provided by the offline database to obtain historical fulfillment data of the target entity object; providing the historical fulfillment data, the current fulfillment data, and current fulfillment trend data to the intelligent threshold calculation unit; the intelligent threshold calculation unit is configured to determine a specific time interval corresponding to a current time, calculate an order threshold of the target entity object in the specific time interval according to a preset regulation and control period and the historical fulfillment data, the current fulfillment data and the current fulfillment trend data, and write the order threshold into a message queue; and the trading unit is used for receiving the order threshold value through a message queue and controlling the order flow when generating an order aiming at the target entity object.
Optionally, the method further includes: a log repository; a data extraction unit; the log library is used for storing service logs; the data extraction unit is used for extracting the order information of the target entity object in real time according to the service log, and the obtained real-time order data stream is transmitted to the dynamic database; and obtaining historical order information of the target entity object according to a preset period and a service log, and storing the obtained historical order information in the offline database.
An embodiment of the present application further provides an order flow control device, including: a fulfillment data obtaining unit, configured to obtain historical fulfillment data, current fulfillment data, and current fulfillment trend data of the target entity object; an order threshold determining unit, configured to determine an order threshold of the target entity object in a specific time interval according to the historical fulfillment data, the current fulfillment data, and the current fulfillment trend data, where the order threshold is a threshold used to set an upper limit of an order quantity generated in the specific time interval; and the order flow control unit is used for limiting the number of orders generated by the target entity object in the specific time interval according to the order threshold.
An embodiment of the present application further provides an electronic device, including: a memory, and a processor; the memory is used for storing a computer program, and the computer program is executed by the processor to execute the method provided by the embodiment of the application.
The embodiment of the present application further provides a storage device, in which a computer program is stored, and the computer program is executed by the processor to perform the method provided in the embodiment of the present application.
Compared with the prior art, the method has the following advantages:
according to the order flow control method, the order flow control device and the order flow control equipment, an order threshold value of the target entity object in a specific time interval is determined according to historical fulfillment data, current fulfillment data and current fulfillment trend data; and limiting the number of orders generated by the target entity object in the specific time interval according to the order threshold. The current performance data and the current performance trend data are used as calculation factors, so that the exploration capacity for the order threshold value can be increased, the excessive dependence on the historical statistical threshold value is avoided, and the order threshold value is not limited to the historical order threshold value. Furthermore, historical fulfillment data are obtained based on offline data, current fulfillment data are obtained based on online data obtained by extracting order information in real time, and the order threshold value is adjusted in real time by combining the offline data with the online data, so that the actual fulfillment condition can be responded in time, the order threshold value is finely enlarged or reduced in real time, and the accuracy of the order threshold value of the target entity object in a specific time interval is improved. The order threshold value obtained through real-time adjustment is provided for a unit for generating orders through a message queue so as to carry out order receiving and flow limiting, and the problem that the flow control accuracy of order generation of a target entity object is low is solved.
The order flow control system comprises a dynamic database, an offline database, a data processing unit, an intelligent threshold value calculating unit and a trading unit, wherein the intelligent threshold value calculating unit determines an order threshold value of a target entity object in a specific time interval according to three factors of historical fulfillment data, current fulfillment data and current fulfillment trend data, and limits the number of orders generated by the target entity object in the specific time interval according to the order threshold value. The current performance data and the current performance trend data are used as calculation factors, so that the exploration capacity for the order threshold value can be increased, the excessive dependence on the historical statistical threshold value is avoided, and the order threshold value is not limited to the historical order threshold value. Furthermore, historical fulfillment data are obtained based on offline data, current fulfillment data are obtained based on online data obtained by extracting order information in real time, and the order threshold value is adjusted in real time through combination of the offline data and the online data, so that the actual fulfillment condition can be responded in time, the order threshold value is finely enlarged or reduced in real time, and the accuracy of the order threshold value of the target entity object in a specific time interval is improved. The order threshold value obtained through real-time adjustment is provided for the transaction unit through the message queue to carry out order taking and flow limiting, and the problem that the flow control accuracy of order generation of the target entity object is low is solved.
Drawings
Fig. 1 is a schematic system environment diagram of a order flow control method provided in an embodiment of the present application;
fig. 2 is a processing flow chart of an order flow control method according to a first embodiment of the present application;
FIG. 3 is an engineering architecture diagram for order flow control according to a first embodiment of the present application;
fig. 4 is a schematic view of an order flow control system according to a second embodiment of the present application;
FIG. 5 is a schematic diagram of an order flow control apparatus according to a third embodiment of the present application;
fig. 6 is a schematic diagram of an electronic device provided herein.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.
The embodiment of the application provides a order flow control method and device, electronic equipment and storage equipment. The embodiment of the application also provides an order flow control system. Details are described in the following examples one by one.
For ease of understanding, the system environment provided by the embodiments of the present application is first presented. Referring to fig. 1, the drawings include: the system comprises a dynamic database 101, an offline database 102, a first flow computing unit 103, a second flow computing unit 104, a business transaction unit 105, a log library 106 and a message queue 107. Wherein the dynamic database and the offline database are two data sources provided to the first streaming computing unit. The dynamic database is used to provide a real-time order data stream of the entity object from which online current fulfillment information, i.e., real-time fulfillment data, may be obtained. The offline database is used to provide an offline order data stream for the entity object from which historical fulfillment information may be obtained. The entity object refers to a service provider, such as a merchant, which provides a service object to a user through a service transaction unit. The first streaming computing unit and the second streaming computing platform are streaming data based computing platforms. The first flow type calculation is processed according to the flow data provided by the two data sources, the online current fulfillment information provided by the dynamic database is obtained, and the current fulfillment data and the current fulfillment trend data of the target entity object are obtained; and acquiring offline historical fulfillment information provided by the offline database to obtain historical fulfillment data of the target entity object. And providing the fulfillment data and the fulfillment trend data to a second streaming computing unit. The second streaming calculation unit determines a specific time interval corresponding to the current time, calculates an order threshold of the target entity object in the specific time interval according to the historical fulfillment data, the current fulfillment data and the current fulfillment trend data and a preset regulation and control period, and provides the order threshold to the business transaction unit, and specifically, the order threshold may be provided through a message queue. And the business transaction unit uses the order threshold value to control order taking flow when generating an order after receiving order placing data provided by the user computing equipment. The order flow control threshold is an order flow control threshold used for limiting the order quantity of the business transaction unit generated orders in a specific time interval, namely, used for controlling the flow of the order receiving quantity of the entity object. The time interval may be an interval divided for one time period at predetermined time intervals. For example, a natural day is divided into 24 time slices in 24 hours, each time slice being a specific time interval. In the process of providing the order threshold value for the business transaction unit by adopting a message queue mechanism, the second streaming calculation unit is a producer of the message and generates the message according to the calculated order threshold value; the business transaction unit is used as a consumer of the message, obtains the message, analyzes the order threshold value and performs order receiving and current limiting. The order threshold value and the order information generated by the business transaction unit are recorded in the log library. The log library can be a memory type database, and online data extracted from the log library in real time is stored in the dynamic database; and periodically performing disk dropping processing on the logs in the log library, and storing offline data included in the disk dropping logs into an offline database. In practical implementation, the first streaming computing unit may be a Blink-based computing platform, and the second streaming computing unit may be a Blink-based computing platform. Of course, other streaming computing platforms such as storm are also possible. The so-called Flink is a stream computation engine that achieves low latency by pipelined data transfer. Blink is a computing platform that implements runtime stability and real-time computation of SQL based on open-source Flink. The Alink is a machine learning algorithm platform realized based on open source Flink, performs complex algorithm operation, and is used for calculating an order threshold value in real time in the embodiment of the application.
The order flow control method provided in the first embodiment of the present application is described below with reference to fig. 2 and 3. The order flow control method shown in fig. 2 includes: step S201 to step S203.
Step S201, obtain the historical performance data, the current performance data and the current performance trend data of the target entity object.
In this embodiment, the order threshold in the specific time interval is calculated based on the historical performance data, the current performance data, and the current performance trend data of the target entity object, so that the matching degree between the order threshold and the order taking capability of the target entity object is more accurate. For example, the preset time interval is half an hour, and the 24 hours per natural day is divided into 48 time slices, and each time slice (half an hour) is a specific time interval. And determining the order threshold value of the specific time slice according to the historical performance data of the specific time slice of the target entity object, the current performance data before the specific time slice and the current performance trend data. The fulfillment data and the fulfillment trend data are extracted from two data sources, namely a historical data source and a real-time data source. The real-time data source may be a real-time service log, and the current fulfillment data of the target entity object is obtained from the real-time data source by the following processes: extracting order information of the target entity object in real time according to the service log, and transmitting the obtained real-time order data stream into a dynamic database; the dynamic database is used for storing order information generated in the current time period from the preset time to the present time; and extracting the order quantity and the order cancellation information of the target entity object from the order information stored in the dynamic database, and obtaining the order non-fulfillment rate in the current time period as the current fulfillment data according to the order quantity and the order cancellation information. The extracted order quantity of the target entity object comprises all generated order quantities, including a fulfillment order and an invalid order, wherein the invalid order is an order which is cancelled without fulfillment. Specifically, the cancellation rate of invalid orders caused by lack of goods, closed by a closed order, insufficient delivery capacity and the like in the current day is counted as current performance data. Further, current performance trend data is calculated based on the current performance data by: dividing the current time period into a plurality of time intervals according to a preset time interval; aiming at the target entity object, acquiring the difference between the order cancellation rates of the current time interval and the previous adjacent time interval as current performance trend data; or, for the target entity object, acquiring a difference between the order cancellation rates of the current time interval and a specified time interval before the current time interval as current performance trend data. Continuing with the above example of time slices, the specific time interval for which the order threshold needs to be calculated is the current time slice, the real-time cancellation rate of the current time slice is the first real-time cancellation rate, the real-time cancellation rate of one or more time slices adjacent to the current time slice is the second real-time cancellation rate, and the difference between the first real-time cancellation rate and the second real-time cancellation rate is calculated as the current performance trend data. If the current time is 10 points, subtracting the real-time cancellation rate of 10 points on the day from the real-time cancellation rate of 9 points on the day to obtain a difference value, and determining the current performance trend data according to the positive and negative of the difference value, so as to reflect whether the performance condition is better or worse. The historical data source may be a log of transactions logged to disk and/or historical transaction information that has been periodically extracted into an offline database. In this embodiment, the historical fulfillment data of the target entity object is obtained by: acquiring historical order information of the target entity object according to a preset period and a service log, and storing the acquired historical order information into an offline database; and extracting the historical order quantity and the historical order canceling information of the target entity object from the order information stored in the off-line database, and obtaining the historical order non-fulfillment rate of a specified historical time period according to the historical order quantity and the historical order canceling information to serve as the historical fulfillment data. Specifically, the cancellation rate of invalid orders in the history time period of the target entity object, which is caused by shortage of goods, closing, overtime, missed orders, insufficient delivery capacity and the like, is counted as history performance data.
Step S202, determining an order threshold of the target entity object in a specific time interval according to the historical fulfillment data, the current fulfillment data and the current fulfillment trend data, where the order threshold is a threshold for setting an upper limit of an order quantity generated in the specific time interval.
In this embodiment, a specific time interval in which the order threshold needs to be adjusted and controlled is determined according to the personalized performance duration of the target entity object. And further specifically calculating a regulated order threshold value according to the service type of the target entity object. Specifically, the method comprises the following steps of: determining a pre-estimated value of the duration of performance of the target entity object according to the historical performance data; and determining a specific time interval corresponding to the current time according to the achievement duration estimated value. Specifically, a time interval corresponding to the current time plus the estimated value of the performance duration is determined and used as a specific time interval in which the order threshold value needs to be regulated and controlled. The contract duration refers to a duration from the time when the business transaction unit generates an order according to order data provided by the user computing equipment to the time when the business object corresponding to the order is completely distributed. And generating an order to identify the order of the target entity object, and completing the distribution of the business object to identify the order to be fulfilled. Specifically, the predicted value of the duration of performance is obtained by the following processing: obtaining historical picking duration and historical distribution duration of the target entity object according to the historical fulfillment data of the target entity object; and estimating a current performing duration pre-estimated value according to the historical picking duration and the historical distribution duration. In practical application, the duration of performance after the order is received by the entity object includes the sum of the picking duration and the distribution duration. And estimating the current day performance duration according to the historical picking duration and the distribution duration, and determining the time slice which needs to actually regulate and control the order threshold value according to the time slice corresponding to the current time. For example, the current time is 14: 10, if the estimated duration of the performance is 90 minutes, the current performance time slice is 15: 40, real-time regulation 15: 30-16: 00 order threshold for time slice. In this embodiment, after a specific time interval in which the order threshold needs to be adjusted and controlled is determined, the order threshold of the specific time interval is calculated in real time through the following processing: determining an initial value of an order threshold value of the target entity object in the specific time interval according to historical performance data corresponding to the specific time interval; adjusting the initial value of the order threshold value according to a preset adjusting and controlling period by adopting a specific algorithm to obtain the order threshold value of the target entity object in a specific time interval; the specific algorithm is an algorithm for adjusting the order threshold according to the historical performance data, the current performance data and the current performance trend data. For example, the order threshold for the current time slice is calculated using the following formula:
thresholdnow_bucket＝Ca*P+b*I+c*D
wherein:
c-slice initial threshold
Figure BDA0002482583980000091
Figure BDA0002482583980000092
Figure BDA0002482583980000093
Figure BDA0002482583980000094
Figure BDA0002482583980000095
Figure BDA0002482583980000096
D＝expand_ratio*(today_cancel_ratelast_bucket-today_cancel_ratenow_bucket)
subject to：
(now _ bucket, last _ bucket) E (1, 48) is the current time slice, and last _ bucket is the last time slice of now _ bucket
expand_ratio∈(0，100]
a∈(0，1]
b∈(0，1]
c∈(0，1]
In this embodiment, the method further includes: determining the service type of the target entity object; and adjusting the initial value of the order threshold value according to the service type of the target entity object to obtain the real-time order threshold value of the specific time interval. The method specifically comprises the following steps: obtaining the proportion of the ideal cancellation rate and the historical smooth cancellation rate of the service type as a first factor; obtaining the proportion of the ideal cancellation rate and the current cancellation rate of the service type as a second factor; obtaining an expansion value of the current performance trend data as a third factor; determining an index for calculating a real-time order threshold according to the first factor and the weight corresponding to the first factor, the second factor and the weight corresponding to the second factor, and the third factor and the weight corresponding to the third factor; and taking the initial value of the order threshold value as a base number, and calculating by using the index to obtain the real-time order threshold value of the specific time interval. Wherein the extension value of the current performance trend data may be obtained based on a difference between a cancellation rate of a time interval immediately preceding the current time interval and a cancellation rate of the current time interval, for example, the current performance trend data is obtained by multiplying the difference by a preset extension ratio. In practical implementation, the following formula can be used to calculate the real-time order threshold value for a specific time interval:
T＝Ca*P+b*I+c*D；
wherein T is a real-time order threshold value of a specific time interval; c is the initial value of the order threshold; a, b and c are respectively P, I, D corresponding weights; p is the proportion of the ideal cancellation rate and the historical smooth cancellation rate of the service type; i is the proportion of the ideal cancellation rate of the service type to the current cancellation rate; d is the expansion value of the current performance trend data.
Historical fulfillment data is obtained based on the offline data, current fulfillment data is obtained based on online data obtained by extracting order information in real time, and the order threshold value is adjusted in real time by combining the offline data with the online data, so that the actual fulfillment condition can be responded in time. The order threshold value can be amplified or reduced in real time through the formula, rather than simply doubled or reduced in proportion, so that the adjustment granularity of the order threshold value is finer, and the accuracy of the order threshold value of the target entity object in a specific time interval is improved.
Step S203, limiting the number of orders generated by the target entity object in the specific time interval according to the order threshold.
In this embodiment, when generating an order, it is determined whether the number of orders in the specific time interval has reached the order threshold, if not, the order is generated normally, otherwise, the order generation is rejected or the order generation is delayed, so that the control of receiving orders for the target entity object is realized. The order flow control threshold value is the order flow control threshold value. In particular implementation, the order threshold value can be provided to the order generation unit of the user generated order through the message queue. The order generation unit may be a business transaction unit or a sub-unit of a business transaction unit. The method comprises the following steps: generating a message according to the calculated order threshold value, and providing the message to an order generating unit through a message queue; and the order generating unit acquires the message, analyzes an order threshold value and performs order taking and current limiting. Specifically, if it is determined that a difference exists between the newly generated order threshold value and the order threshold value of the last regulation and control cycle, an order threshold value updating message is generated according to the newly generated order threshold value, and the updating message is provided to an order generating unit for generating an order for the target entity object through a message queue; the newly generated order threshold is parsed out and used for limiting the number of orders generated in a specific time interval. In this embodiment, the method further includes, for the order threshold and generating an order information recording log, storing the log in a memory database, and performing log destaging processing before storing information recorded in the log in an offline database, and storing the log in a magnetic disk for log destaging processing. Further, online data can be extracted from log information recorded in a memory database in real time and stored in a dynamic database; and periodically performing log disk-dropping processing on log information recorded in the memory database, and storing offline data included in the disk-dropping log in an offline database.
In this embodiment, the method further includes performing forced activation on the target entity object, specifically, if the target entity object is limited to receive orders due to the order threshold limit, detecting a trigger for the forced activation control at a fixed time, and expanding the order threshold or canceling the order limit according to a preset ratio according to the trigger. Further, if the target entity object is determined to be a specific level entity object, receiving an input trigger of order threshold control for the target entity object, and canceling the order flow control according to the input trigger. Therefore, the order threshold value control is more flexible, and a loose control mode of releasing the order threshold value at fixed time can be adopted for entity objects with good performance records. The order threshold is regulated and controlled in a flexible mode, and the order is fed back to a unit for generating the order in time through the message queue for order taking and flow limiting, so that the flow control accuracy of order generation is improved, the matching degree of the flow limiting effect of the order threshold and the performance capability of a target entity object is higher, and the user experience and the experience of a delivery party for implementing delivery are improved.
Referring to fig. 3, the engineering architecture shown in the figure includes: the system comprises a memory database 101, an ODPS 302, an Hbase303, a Blink 304, an Alink 305, a message queue 306, a service transaction unit 307 and a merchant forced activation unit 308. The memory database is a database for storing a real-time log, and a real-time order data stream of an entity object (merchant) can be extracted from the real-time log, so that online current performance data can be further acquired. The ODPS is an offline database, historical order information is stored, in practical application, the log in the memory database is subjected to tray drop processing periodically, and after the tray drop processing, the order data of the entity object included in the tray drop log is stored in the ODPS database as offline data. Hbase is a distributed storage engine, acquires historical order information from ODPS to generate an offline data stream, and further provides historical fulfillment information to Blink. Blink is used for acquiring a real-time order data stream provided by a memory database and an offline order data stream provided by Hbase, and obtaining current performance data and current performance trend data of an entity object based on the real-time order data stream; historical fulfillment data for the entity object is obtained based on the offline order data stream. The Alink obtains the performance data and the performance trend data output by the Blink, calculates the order threshold value of the current time slice, and outputs the order threshold value. The Alink generates a message according to the order threshold value and provides the message to a message queue. And the business transaction unit acquires the message, analyzes an order threshold value, and limits the number of orders generated by the current time slice by using the order threshold value, thereby realizing the control of order receiving flow of the merchant. And recording logs according to the order threshold value and order information generated by the service transaction unit, storing the real-time logs in a memory database, and storing information included in the log after the log is landed as offline data in an offline database. And the merchant forced activation unit detects at regular time, displays the order receiving limiting information if detecting that the merchant at a specific level is limited to receive orders, and enlarges the order threshold value or cancels the order receiving limitation according to a preset proportion according to the trigger if further detecting the trigger aiming at the forced activation control.
Thus, a method provided by a first embodiment is described, which determines an order threshold of the target entity object in a specific time interval according to historical fulfillment data, current fulfillment data and current fulfillment trend data; and limiting the number of orders generated by the target entity object in the specific time interval according to the order threshold. The current performance data and the current performance trend data are used as calculation factors, so that the exploration capacity for the order threshold value can be increased, the excessive dependence on the historical statistical threshold value is avoided, and the order threshold value is not limited to the historical order threshold value. Furthermore, historical fulfillment data are obtained based on offline data, current fulfillment data are obtained based on online data obtained by extracting order information in real time, and the order threshold value is adjusted in real time by combining the offline data with the online data, so that the actual fulfillment condition can be responded in time, the order threshold value is finely enlarged or reduced in real time, and the accuracy of the order threshold value of the target entity object in a specific time interval is improved. The order threshold value obtained through real-time adjustment is provided for a unit for generating orders through a message queue so as to carry out order receiving and flow limiting, and the problem that the flow control accuracy of order generation of a target entity object is low is solved.
Based on the foregoing embodiments, a second embodiment of the present application provides an order flow control system. The system provided by the second embodiment is described below with reference to fig. 4, and for related parts, reference is made to the description of corresponding parts of the above embodiments. The order flow control system shown in fig. 4 includes: the system comprises a dynamic database 401, an offline database 402, a data processing unit 403, an intelligent threshold calculation unit 404 and a transaction unit 405.
And the dynamic database is used for storing a real-time order data stream of the target entity object, and providing online current performance information by matching with the data processing unit. In practical applications, the dynamic database may be an in-memory database, such as timeten.
The off-line database is used for storing the off-line order data stream of the target entity object and providing off-line historical performance information by matching the data processing unit. In particular, the offline database may employ ODPS, and historical order data in ODPS is provided to the data processing unit by way of an offline order data stream via the Hbase distributed data storage engine.
The data processing unit is used for acquiring online current fulfillment information provided by the dynamic database to obtain current fulfillment data and current fulfillment trend data of the target entity object; acquiring offline historical fulfillment information provided by the offline database to obtain historical fulfillment data of the target entity object; providing the historical fulfillment data, the current fulfillment data, and current fulfillment trend data to the intelligent threshold calculation unit. In specific implementation, a Blink or other streaming computing platform may be adopted to implement the data processing unit, and perform data processing on the real-time order data stream from the in-memory database and the offline order data stream from the offline database to obtain current performance data, current performance trend data, and historical performance data of the target entity object, and provide the current performance data, the current performance trend data, and the historical performance data to the intelligent threshold computing unit.
The intelligent threshold calculation unit is configured to determine a specific time interval corresponding to a current time, calculate an order threshold of the target entity object in the specific time interval according to a preset regulation and control period and the historical fulfillment data, the current fulfillment data and the current fulfillment trend data, and write the order threshold into a message queue. In specific implementation, the intelligent threshold calculation function can be realized by adopting Alink or other streaming calculation platforms. The method comprises the following steps: determining a specific time interval in which an order threshold value needs to be regulated and controlled and an order threshold value initial value of the specific time interval; determining the service type of the target entity object; and adjusting the initial value of the order threshold value according to the service type of the target entity object to obtain the real-time order threshold value of the specific time interval. The initial value of the order threshold value can be obtained according to the order threshold value of the historical time interval corresponding to the specific time interval. For example, the average value of the order threshold values of a plurality of historical time intervals is used as the order threshold initial value. The method specifically comprises the following steps: obtaining the proportion of the ideal cancellation rate and the historical smooth cancellation rate of the service type as a first factor; obtaining the proportion of the ideal cancellation rate and the current cancellation rate of the service type as a second factor; obtaining an expansion value of the current performance trend data as a third factor; determining an index for calculating a real-time order threshold according to the first factor and the weight corresponding to the first factor, the second factor and the weight corresponding to the second factor, and the third factor and the weight corresponding to the third factor; and taking the initial value of the order threshold value as a base number, and calculating by using the index to obtain the real-time order threshold value of the specific time interval. Wherein the extension value of the current performance trend data may be obtained based on a difference between a cancellation rate of a time interval immediately preceding the current time interval and a cancellation rate of the current time interval, for example, the current performance trend data is obtained by multiplying the difference by a preset extension ratio. In practical implementation, the following formula can be used to calculate the real-time order threshold value for a specific time interval:
T＝Ca*P+b*I+c*D；
wherein T is a real-time order threshold value of a specific time interval; c is the initial value of the order threshold; a, b and c are respectively P, I, D corresponding weights; p is the proportion of the ideal cancellation rate and the historical smooth cancellation rate of the service type; i is the proportion of the ideal cancellation rate of the service type to the current cancellation rate; d is the expansion value of the current performance trend data. Further, the intelligent threshold calculation unit outputs the order threshold. Specifically, a message is generated according to the order threshold value, and the message is provided to a message queue. An MQ message queue mechanism may be employed. Historical fulfillment data is obtained based on the offline data, current fulfillment data is obtained based on online data obtained by extracting order information in real time, and the order threshold value is adjusted in real time by combining the offline data with the online data, so that the actual fulfillment condition can be responded in time. The order threshold value can be amplified or reduced in real time through the formula, rather than simply doubled or reduced in proportion, so that the adjustment granularity of the order threshold value is finer, and the accuracy of the order threshold value of the target entity object in a specific time interval is improved.
And the trading unit is used for receiving the order threshold value through a message queue and controlling the order flow when generating an order aiming at the target entity object. Specifically, the transaction unit obtains the message generated by the intelligent threshold calculation unit, analyzes the order threshold, and limits the number of orders generated in the specific time interval (for example, the current time slice) by using the order threshold, thereby realizing the control of the order receiving flow of the entity object. Order threshold values obtained through flexible adjustment are obtained in time through the message queue, order taking and flow limiting are carried out, and flow control accuracy of order generation is improved, so that matching degree of flow limiting effect of the order threshold values and performance capability of target entity objects is higher, and user experience is improved.
In this embodiment, the system further includes: a log repository; a data extraction unit; and the log library is used for storing the service log. In specific implementation, a memory database is used as the log library, and logs are recorded according to processing information such as order threshold values, order generation, order cancellation and the like. The data extraction unit is used for extracting the order information of the target entity object in real time according to the service log, and the obtained real-time order data stream is transmitted to the dynamic database; and performing tray falling processing on the service logs in the memory database according to a preset period, and storing the order data serving as historical data into an offline database after the tray falling processing. Further the system further comprises: the cell is forcibly activated. The forced activation unit is used for detecting whether the merchants with specific levels are limited to take orders or not at regular time; if yes, showing the order taking limiting information, and if further detecting the trigger for the forced activation control, expanding the order threshold value according to the trigger in a preset proportion or canceling the order taking limitation.
To this end, the system provided in this embodiment is described, where the system determines an order threshold of the target entity object in a specific time interval according to three factors, i.e., historical fulfillment data, current fulfillment data, and current fulfillment trend data, and limits the number of orders generated by the target entity object in the specific time interval according to the order threshold. The current performance data and the current performance trend data are used as calculation factors, so that the exploration capacity for the order threshold value can be increased, the excessive dependence on the historical statistical threshold value is avoided, and the order threshold value is not limited to the historical order threshold value. Furthermore, historical fulfillment data are obtained based on offline data, current fulfillment data are obtained based on online data obtained by extracting order information in real time, and the order threshold value is adjusted in real time by combining the offline data with the online data, so that the actual fulfillment condition can be responded in time, the order threshold value is finely enlarged or reduced in real time, and the accuracy of the order threshold value of the target entity object in a specific time interval is improved. The order threshold value obtained through real-time adjustment is provided for a unit for generating orders through a message queue so as to carry out order receiving and flow limiting, and the problem that the flow control accuracy of order generation of a target entity object is low is solved.
A third embodiment of the present application provides an order flow control apparatus corresponding to the first embodiment. The device is described below with reference to fig. 5. The order flow control apparatus shown in fig. 5 includes:
a fulfillment data obtaining unit 501, configured to obtain historical fulfillment data, current fulfillment data, and current fulfillment trend data of the target entity object;
an order threshold determining unit 502, configured to determine an order threshold of the target entity object in a specific time interval according to the historical fulfillment data, the current fulfillment data, and the current fulfillment trend data, where the order threshold is a threshold used to set an upper limit of an order quantity generated in the specific time interval;
the order flow control unit 503 is configured to limit the number of orders generated by the target entity object in the specific time interval according to the order threshold.
Optionally, the fulfillment data obtaining unit 501 is specifically configured to: extracting order information of the target entity object in real time according to the service log, and transmitting the obtained real-time order data stream into a dynamic database; the dynamic database is used for storing order information generated in the current time period from the preset time to the present time; and extracting the order quantity and the order cancellation information of the target entity object from the order information stored in the dynamic database, and obtaining the order non-fulfillment rate in the current time period as the current fulfillment data according to the order quantity and the order cancellation information.
Optionally, the fulfillment data obtaining unit 501 is specifically configured to: acquiring historical order information of the target entity object according to a preset period and a service log, and storing the acquired historical order information into an offline database; and extracting the historical order quantity and the historical order canceling information of the target entity object from the order information stored in the off-line database, and obtaining the historical order non-fulfillment rate of a specified historical time period according to the historical order quantity and the historical order canceling information to serve as the historical fulfillment data.
Optionally, the fulfillment data obtaining unit 501 is specifically configured to: dividing the current time period into a plurality of time intervals according to a preset time interval; aiming at the target entity object, acquiring the difference between the order cancellation rates of the current time interval and the previous adjacent time interval as current performance trend data; or, for the target entity object, acquiring a difference between the order cancellation rates of the current time interval and a specified time interval before the current time interval as current performance trend data.
Optionally, the order threshold determining unit 502 is specifically configured to: determining a pre-estimated value of the duration of performance of the target entity object according to the historical performance data; and determining a specific time interval corresponding to the current time according to the achievement duration estimated value.
Optionally, the order threshold determining unit 502 is specifically configured to: obtaining historical picking duration and historical distribution duration of the target entity object according to the historical fulfillment data of the target entity object; and estimating a current performing duration pre-estimated value according to the historical picking duration and the historical distribution duration.
Optionally, the order threshold determining unit 502 is specifically configured to: determining an initial value of an order threshold value of the target entity object in the specific time interval according to historical performance data corresponding to the specific time interval; adjusting the initial value of the order threshold value according to a preset adjusting and controlling period by adopting a specific algorithm to obtain the order threshold value of the target entity object in a specific time interval; the specific algorithm is an algorithm for adjusting the order threshold according to the historical performance data, the current performance data and the current performance trend data.
Optionally, the order threshold determining unit 502 is specifically configured to: determining the service type of the target entity object; and adjusting the initial value of the order threshold value according to the service type of the target entity object to obtain the real-time order threshold value of the specific time interval.
Optionally, the order threshold determining unit 502 is specifically configured to: obtaining the proportion of the ideal cancellation rate and the historical smooth cancellation rate of the service type as a first factor; obtaining the proportion of the ideal cancellation rate and the current cancellation rate of the service type as a second factor; obtaining an expansion value of the current performance trend data as a third factor; determining an index for calculating a real-time order threshold according to the first factor and the weight corresponding to the first factor, the second factor and the weight corresponding to the second factor, and the third factor and the weight corresponding to the third factor; and taking the initial value of the order threshold value as a base number, and calculating by using the index to obtain the real-time order threshold value of the specific time interval.
Optionally, the order flow control unit 503 is specifically configured to: if the difference between the newly generated order threshold value and the order threshold value of the last regulation and control period is determined, generating an order threshold value updating message according to the newly generated order threshold value, and providing the updating message to an order generating unit for generating an order aiming at the target entity object through a message queue; the newly generated order threshold is parsed out and used for limiting the number of orders generated in a specific time interval.
Optionally, the order flow control unit 503 is specifically configured to: and if the target entity object is determined to be the entity object with the specific level, receiving an input trigger of order threshold control aiming at the target entity object, and canceling the order flow control according to the input trigger.
So far, the apparatus provided in this embodiment is explained, and the apparatus determines the order threshold of the target entity object in a specific time interval according to the historical fulfillment data, the current fulfillment data and the current fulfillment trend data; and limiting the number of orders generated by the target entity object in the specific time interval according to the order threshold. The current performance data and the current performance trend data are used as calculation factors, so that the exploration capacity for the order threshold value can be increased, the excessive dependence on the historical statistical threshold value is avoided, and the order threshold value is not limited to the historical order threshold value. Furthermore, historical fulfillment data are obtained based on offline data, current fulfillment data are obtained based on online data obtained by extracting order information in real time, and the order threshold value is adjusted in real time by combining the offline data with the online data, so that the actual fulfillment condition can be responded in time, the order threshold value is finely enlarged or reduced in real time, and the accuracy of the order threshold value of the target entity object in a specific time interval is improved. The order threshold value obtained through real-time adjustment is provided for a unit for generating orders through a message queue so as to carry out order receiving and flow limiting, and the problem that the flow control accuracy of order generation of a target entity object is low is solved.
Based on the above embodiments, a fourth embodiment of the present application provides an electronic device. Fig. 6 is a schematic diagram of the electronic device, which includes: a memory 601, and a processor 602; the memory is used for storing a computer program, and the computer program is executed by the processor to execute the order flow control method provided by the embodiment of the application.
To this end, the electronic device provided in this embodiment is described, where the electronic device determines an order threshold of the target entity object in a specific time interval according to historical fulfillment data, current fulfillment data, and current fulfillment trend data; and limiting the number of orders generated by the target entity object in the specific time interval according to the order threshold. The current performance data and the current performance trend data are used as calculation factors, so that the exploration capacity for the order threshold value can be increased, the excessive dependence on the historical statistical threshold value is avoided, and the order threshold value is not limited to the historical order threshold value. The historical fulfillment data, the current fulfillment data and the current fulfillment trend data are further combined, the order threshold value is adjusted in real time according to the preset regulation and control period, the order threshold value can be enlarged or reduced in real time, the response is timely performed, the accuracy of the order threshold value of the target entity object in a specific time interval is improved, and the problem that the matching degree of the order threshold value and the fulfillment capacity of the target entity object is low is solved.
Based on the foregoing embodiments, a fifth embodiment of the present application provides a storage device, and please refer to the corresponding description of the foregoing embodiments for related parts. The schematic diagram of the storage device is similar to fig. 6. The storage device stores a computer program, and the computer program is executed by the processor to execute the order flow control method provided by the embodiment of the application.
To this end, the storage device provided in this embodiment is described, where the storage device stores instructions that determine an order threshold of the target entity object in a specific time interval according to historical fulfillment data, current fulfillment data, and current fulfillment trend data; and limiting the number of orders generated by the target entity object in the specific time interval according to the order threshold. The current performance data and the current performance trend data are used as calculation factors, so that the exploration capacity for the order threshold value can be increased, the excessive dependence on the historical statistical threshold value is avoided, and the order threshold value is not limited to the historical order threshold value. Furthermore, historical fulfillment data are obtained based on offline data, current fulfillment data are obtained based on online data obtained by extracting order information in real time, and the order threshold value is adjusted in real time by combining the offline data with the online data, so that the actual fulfillment condition can be responded in time, the order threshold value is finely enlarged or reduced in real time, and the accuracy of the order threshold value of the target entity object in a specific time interval is improved. The order threshold value obtained through real-time adjustment is provided for a unit for generating orders through a message queue so as to carry out order receiving and flow limiting, and the problem that the flow control accuracy of order generation of a target entity object is low is solved.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
1. Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.
2. As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application, therefore, the scope of the present application should be determined by the claims that follow.

Claims (10)

1. A order flow control method is characterized by comprising the following steps:
obtaining historical performance data, current performance data and current performance trend data of the target entity object;
determining an order threshold of the target entity object in a specific time interval according to the historical fulfillment data, the current fulfillment data and the current fulfillment trend data, wherein the order threshold is a threshold for setting an upper limit of an order quantity generated in the specific time interval;
and limiting the number of orders generated by the target entity object in the specific time interval according to the order threshold.
2. The method of claim 1, wherein obtaining current performance data for a target entity object comprises:
extracting order information of the target entity object in real time according to the service log, and transmitting the obtained real-time order data stream into a dynamic database; the dynamic database is used for storing order information generated in the current time period from the preset time to the present time;
and extracting the order quantity and the order cancellation information of the target entity object from the order information stored in the dynamic database, and obtaining the order non-fulfillment rate in the current time period as the current fulfillment data according to the order quantity and the order cancellation information.
3. The method of claim 1, wherein obtaining historical fulfillment data for a target entity object comprises:
acquiring historical order information of the target entity object according to a preset period and a service log, and storing the acquired historical order information into an offline database;
and extracting the historical order quantity and the historical order canceling information of the target entity object from the order information stored in the off-line database, and obtaining the historical order non-fulfillment rate of a specified historical time period according to the historical order quantity and the historical order canceling information to serve as the historical fulfillment data.
4. The method of claim 1, wherein obtaining current performance trend data for the target entity object comprises:
dividing the current time period into a plurality of time intervals according to a preset time interval;
aiming at the target entity object, acquiring the difference between the order cancellation rates of the current time interval and the previous adjacent time interval as current performance trend data; or, for the target entity object, acquiring a difference between the order cancellation rates of the current time interval and a specified time interval before the current time interval as current performance trend data.
5. The method of claim 1, further comprising:
determining a pre-estimated value of the duration of performance of the target entity object according to the historical performance data;
and determining a specific time interval corresponding to the current time according to the achievement duration estimated value.
6. The method of claim 5, wherein determining the predicted value of duration of performance of the target entity object based on the historical performance data comprises:
obtaining historical picking duration and historical distribution duration of the target entity object according to the historical fulfillment data of the target entity object;
and estimating a current performing duration pre-estimated value according to the historical picking duration and the historical distribution duration.
7. The method of claim 1, further comprising:
determining an initial value of an order threshold value of the target entity object in the specific time interval according to historical performance data corresponding to the specific time interval;
the determining the order threshold of the target entity object in the specific time interval includes:
adjusting the initial value of the order threshold value according to a preset adjusting and controlling period by adopting a specific algorithm to obtain the order threshold value of the target entity object in a specific time interval; the specific algorithm is an algorithm for adjusting the order threshold according to the historical performance data, the current performance data and the current performance trend data.
8. The method of claim 7, further comprising:
determining the service type of the target entity object;
and adjusting the initial value of the order threshold value according to the service type of the target entity object to obtain the real-time order threshold value of the specific time interval.
9. The method according to claim 8, wherein the adjusting the initial value of the order threshold according to the service type of the target entity object to obtain the real-time order threshold of the specific time interval comprises:
obtaining the proportion of the ideal cancellation rate and the historical smooth cancellation rate of the service type as a first factor;
obtaining the proportion of the ideal cancellation rate and the current cancellation rate of the service type as a second factor;
obtaining an expansion value of the current performance trend data as a third factor;
determining an index for calculating a real-time order threshold according to the first factor and the weight corresponding to the first factor, the second factor and the weight corresponding to the second factor, and the third factor and the weight corresponding to the third factor;
and taking the initial value of the order threshold value as a base number, and calculating by using the index to obtain the real-time order threshold value of the specific time interval.
10. An order flow control system, comprising: the system comprises a dynamic database, an off-line database, a data processing unit, an intelligent threshold value calculating unit and a transaction unit;
the dynamic database is used for storing a real-time order data stream of a target entity object, and providing online current performance information by matching with the data processing unit;
the off-line database is used for storing an off-line order data stream of the target entity object and providing off-line historical performance information by matching the data processing unit;
the data processing unit is used for acquiring online current fulfillment information provided by the dynamic database to obtain current fulfillment data and current fulfillment trend data of the target entity object; acquiring offline historical fulfillment information provided by the offline database to obtain historical fulfillment data of the target entity object; providing the historical fulfillment data, the current fulfillment data, and current fulfillment trend data to the intelligent threshold calculation unit;
the intelligent threshold calculation unit is configured to determine a specific time interval corresponding to a current time, calculate an order threshold of the target entity object in the specific time interval according to a preset regulation and control period and the historical fulfillment data, the current fulfillment data and the current fulfillment trend data, and write the order threshold into a message queue;
and the trading unit is used for receiving the order threshold value through a message queue and controlling the order flow when generating an order aiming at the target entity object.
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Application publication date: 20200828